Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgements -- Chapter 1 Introduction -- 1.1 The History of Image Registration -- 1.2 Definition of Registration -- 1.3 What is Motion Estimation -- 1.4 Video Quality Assessment -- 1.5 Applications -- 1.5.1 Video Processing -- 1.5.2 Medical Applications -- 1.5.3 Security Applications -- 1.5.4 Military and Satellite Applications -- 1.5.5 Reconstruction Applications -- 1.6 Organization of the Book -- References -- Chapter 2 Registration for Video Coding -- 2.1 Introduction -- 2.2 Motion Estimation Technique -- 2.2.1 Block-Based Motion Estimation Techniques -- 2.3 Registration and Standards for Video Coding -- 2.3.1 H.264 -- 2.3.2 H.265 -- 2.4 Evaluation Criteria -- 2.4.1 Dataset -- 2.4.2 Motion-Compensated Prediction Error (MCPE) in dB -- 2.4.3 Entropy in bpp -- 2.4.4 Angular Error in Degrees -- 2.5 Objective Quality Assessment -- 2.5.1 Full-Reference Quality Assessment -- 2.5.2 No-Reference and Reduced-Reference Quality Metrics -- 2.5.3 Temporal Masking in Video Quality Assessment -- 2.6 Conclusion -- 2.7 Exercises -- References -- Chapter 3 Registration for Motion Estimation and Object Tracking -- 3.1 Introduction -- 3.1.1 Mathematical Notation -- 3.2 Optical Flow -- 3.2.1 Horn-Schunk Method -- 3.2.2 Lukas-Kanade Method -- 3.2.3 Applications of Optical Flow for Motion Estimation -- 3.3 Efficient Discriminative Features for Motion Estimation -- 3.3.1 Invariant Features -- 3.3.2 Optimization Stage -- 3.4 Object Tracking -- 3.4.1 KLT Tracking -- 3.4.2 Motion Filtering -- 3.4.3 Multiple Object Tracking -- 3.5 Evaluating Motion Estimation and Tracking -- 3.5.1 Metrics for Motion Detection -- 3.5.2 Metrics for Motion Tracking -- 3.5.3 Metrics for Efficiency -- 3.5.4 Datasets -- 3.6 Conclusion -- 3.7 Exercise -- References.

Chapter 4 Face Alignment and Recognition Using Registration -- 4.1 Introduction -- 4.2 Unsupervised Alignment Methods -- 4.2.1 Natural Features: Gradient Features -- 4.2.2 Dense Grids: Non-rigid Non-affine Transformations -- 4.3 Supervised Alignment Methods -- 4.3.1 Generative Models -- 4.3.2 Discriminative Approaches -- 4.4 3D Alignment -- 4.4.1 Hausdorff Distance Matching -- 4.4.2 Iterative Closest Point (ICP) -- 4.4.3 Multistage Alignment -- 4.5 Metrics for Evaluation -- 4.5.1 Evaluating Face Recognition -- 4.5.2 Evaluating Face Alignment -- 4.5.3 Testing Protocols and Benchmarks -- 4.5.4 Datasets -- 4.6 Conclusion -- 4.7 Exercise -- References -- Chapter 5 Remote Sensing Image Registration in the Frequency Domain -- 5.1 Introduction -- 5.2 Challenges in Remote Sensing Imaging -- 5.3 Satellite Image Registration in the Fourier Domain -- 5.3.1 Translation Estimation Using Correlation -- 5.4 Correlation Methods -- 5.5 Subpixel Shift Estimation in the Fourier Domain -- 5.6 FFT-Based Scale-Invariant Image Registration -- 5.7 Motion Estimation in the Frequency Domain for Remote Sensing Image Sequences -- 5.7.1 Quad-Tree Phase Correlation -- 5.7.2 Shape Adaptive Motion Estimation in the Frequency Domain -- 5.7.3 Optical Flow in the Fourier Domain -- 5.8 Evaluation Process and Related Datasets -- 5.8.1 Remote Sensing Image Datasets -- 5.9 Conclusion -- 5.10 Exercise-Practice -- References -- Chapter 6 Structure from Motion -- 6.1 Introduction -- 6.2 Pinhole Model -- 6.3 Camera Calibration -- 6.4 Correspondence Problem -- 6.5 Epipolar Geometry -- 6.6 Projection Matrix Recovery -- 6.6.1 Triangulation -- 6.7 Feature Detection and Registration -- 6.7.1 Auto-correlation -- 6.7.2 Harris Detector -- 6.7.3 SIFT Feature Detector -- 6.8 Reconstruction of 3D Structure and Motion.

6.8.1 Simultaneous Localization and Mapping -- 6.8.2 Registration for Panoramic View -- 6.9 Metrics and Datasets -- 6.9.1 Datasets for Performance Evaluation -- 6.10 Conclusion -- 6.11 Exercise-Practice -- References -- Chapter 7 Medical Image Registration Measures -- 7.1 Introduction -- 7.2 Feature-Based Registration -- 7.2.1 Generalized Iterative Closest Point Algorithm -- 7.2.2 Hierarchical Maximization -- 7.3 Intensity-Based Registration -- 7.3.1 Voxels as Features -- 7.3.2 Special Case: Spatially Determined Correspondences -- 7.3.3 Intensity Difference Measures -- 7.3.4 Correlation Coefficient -- 7.3.5 Pseudo-likelihood Measures -- 7.3.6 General Implementation Using Joint Histograms -- 7.4 Transformation Spaces and Optimization -- 7.4.1 Rigid Transformations -- 7.4.2 Similarity Transformations -- 7.4.3 Affine Transformations -- 7.4.4 Projective Transformations -- 7.4.5 Polyaffine Transformations -- 7.4.6 Free-Form Transformations: 'Small Deformation' Model -- 7.4.7 Free-Form Transformations: 'Large Deformation' Models -- 7.5 Conclusion -- 7.6 Exercise -- 7.6.1 Implementation Guidelines -- References -- Chapter 8 Video Restoration Using Motion Information -- 8.1 Introduction -- 8.2 History of Video and Film Restoration -- 8.3 Restoration of Video Noise and Grain -- 8.4 Restoration Algorithms for Video Noise -- 8.5 Instability Correction Using Registration -- 8.6 Estimating and Removing Flickering -- 8.7 Dirt Removal in Video Sequences -- 8.8 Metrics in Video Restoration -- 8.9 Conclusions -- 8.10 Exercise-Practice -- References -- Index -- EULA.